The Telegraph covered a robotic
revolution in the healthcare sector and predicted an increase in robotic
systems in hospitals in the coming decade. Insights from 2016 indicate that about 86% of healthcare provider
organizations and technology vendors to healthcare are using artificial
intelligence technology. Institutions across the globe are adapting to
automation, machine learning, and artificial intelligence (AI) including
doctors, hospitals, insurance companies, and industries with ties to
healthcare.
Here are a few of the
many ways AI and data analytics are paving the road to better healthcare

1. Mining Medical Records and Devising Treatment Plans

In a day, a radiologist attends to
almost 200 patients and 3000 medical images. Today, every person who visits a
medical practitioner has their medical record created. The number of records
will only grow in the coming years. Analysing this data and determining a
treatment plan consumes valuable time. AI can help reduce the workload and
expedite the medical process with the help of something called as a Patient Data
Mining.

MIT’s Computer Science and
Artificial Intelligence Laboratory (CSAIL) created ‘ICU Intervene’, a
machine-learning approach that collects a significant amount of ICU data
ranging from medical to demographic details. Through this data, the AI can
determine the types of treatment the patients need and quicken the diagnosis to
save critical time.

A community hospital in Florida,
the Flagler
Hospital has created a CarePath for every patient admitted from
Pneumonia or Sepsis. This CarePath has traced a Data Group called the
Goldilocks groups, which had the lowest length of stay, lowest readmission
rate, and lowest cost paid. The process followed for these patients led to the
best possible outcomes. This helped the hospital trace what should ideally happen
in the emergency room in a sequence.

This pathway is expected to cut
length of stay by two days and save $1,356 per Pneumonia patient. They also
found the readmission rate reduced from 2.9% to 0.4% of total patients.

“Data gathered and
presented by AI algorithms will enable healthcare providers and doctors to see
patients’ health risks and take more precise, early action to prevent, lessen
the impact of or forestall disease progression. These interventions will curb
healthcare costs and lead to improved patient health outcomes,” said Derek
Gordon, COO of Lumiat, to Cygnismedia.

2. Assisting in Repetitive Jobs and Future Prediction

Routine jobs such as X-rays, CT
scans, and data entry can be offloaded to an AI assistant.

In cardiology and radiology, not
only does analysis and compilation of data consume crucial time but is also
prone to trial and error. AI can prove to be more accurate and helpful in such
scenarios. It can read CT scans and medical reports to provide a diagnosis of
similar images stored in the database.

In fact, a Chicago start-up,
Careskore uses a cloud-based predictive analytics platform. Using Zeus
algorithm in real time, Careskore predicts the likeliness of an individual’s
hospitalisation after studying a range of data which includes a combination of
behavioural, demographic and clinical data.

3. Blending Physical and Virtual Consultations

Chat bots used in the healthcare
sector interact with the patients through telephone, text, or website to
schedule appointments and follow-ups, billing, processing 24×7 urgent requests
for customer care, and so on. They help in reducing the overall administrative
cost of the hospital.

Medical Virtual Assistants (MVA)
collect and compile a patient’s medical and demographic details. M-health apps
help people track their health and notify patients about upcoming appointments.
They are also programmed to answer the basic health-related or medical queries
of a patient.

A great example of MVA is Sensly, it is an avatar-based clinical
app. It helps clinicians, caretakers, and patients better monitor and manage
their health. It has deployed the first fleet of AI powered nurse-avatars to
clinics in San Francisco. It focusses on creating an effective communication
channel to avoid repeated hospital admissions.

4. Medication Management

AI-enabled systems can track
patients’ data and suggest treatments based on analysis. An Israeli start-up
developed AI algorithms closely accurate or even more precise than humans when
it comes to the early detection of conditions such as coronary aneurysms, brain
bleeds, malignant tissue in breast mammography, and osteoporosis. This way AI can become an active
part of clinical-decision making. In a recent article
by Wired, it’s stated that AI is 99% accurate and 30 times faster in
studying and translating mammograms, allowing much earlier detection of breast
cancer than human doctors can. Such assistance can significantly augment the
medical procedure.

In order to monitor the use of
medication by a patient, National Institutes of Health have created an app
called AiCure.
This app uses the phone camera to track the dosage. It has been a significant
contributor when it comes to patient who tend to go against the Doctor’s advise
or suffer from serious medical conditions.

[According to Enlitic, a medical startup, “Until recently, diagnostic computer programs were written using a series of predefined assumptions about disease-specific features. A specialized program had to be designed for each part of the body and only a limited set of diseases could be identified, preventing their flexibility and scalability.”

Also, AI is becoming crucial to improve data documentation and indexing in electronic health record (EHR) systems. Even though the EHR platforms continue to proliferate, navigating and accessing data from these platforms have remained inconvenient for most of the healthcare providers. In fact, most of them find these systems inflexible and costly to configure. AI facilitates accurate data extraction and clinical documentation. Subsequently, it helps delivery networks develop predictive algorithms for health prediction and diagnosis. For instance, Google and Enlitic are working on AI-based image interpretation algorithms. Also, AI-derived EHR platforms render support in making clinical decisions and devising treatment strategies

5.Blockchain Can Enable More Efficient HER

Seamless interoperability of electronic health records (EHR) is crucial for accurate medical data management. However, that’s exactly the problem healthcare providers are frequently facing. According to John Meigs, Jr., MD, Board Chair, American Academy of Family Physicians, “For the most part, the different EHR software programs available don’t talk to each other and in fact make it extremely difficult to exchange data across systems.”
Blockchain can make EHR more convenient and easy to use. Blockchain helps EHR to show date from multiple databases added in the ledger instead of a single data base. Here, blockchain acts as a decentralized control denying any exclusive ownership to data, but at the same time making it available for everyone. Eliminating an organization between the patient and his/her records is the biggest advantage of using blockchain enabling a more secured process of data exchange

6.Finding the right Talent in Healthcare

As the healthcare industry grows,
there is always a need for qualified healthcare professionals. Often, hiring
managers receive hundreds of resumes per open role. When shortlisting
candidates for interviews, they use various data points such as filtering out
candidates with too many or too few years of experience. Beyond this level of
filtering, many companies are using AI chatbot software for recruitment. For
example, Accenture uses Min, an AI virtual recruiter to hire data scientists in Singapore. This helps
recruiters save time, improve efficiency, and make fair hiring decisions. For
candidates, the chatbot engages, interviews, and shortlists them 24/7.

7. Helping People Make Better Health Choices

Based on the demographic,
behavioural, and medical data of people, AI-enabled systems can predict health
risks in advance and warn people accordingly. Six months after El Camino Hospital in Silicon
Valley applied artificial intelligence, the rate of patients with fatal
diseases fell by a 39 percent.

The most popular example of an
instrument helping people lead healthier lives is the FitBit or other
healthcare trackers. They are easily available, trace trends, and
set health targets.

These apps and trackers can
efficiently track a lot of data and guide humans to lead a healthy lifestyle.
On the basis of the demographic, behavioural and medical data of people,
AI-enabled systems can predict health risks in advance and can warn people
accordingly.

As per OECD estimates and figures
from The United States Institute of Medicine, the top 15 countries by
healthcare expenditure waste an average of between $1,100 and $1,700 per person
annually. Health App Solutions offered by AI helps healthcare systems avoid
needless hospitalisations.

Not only does Data Science help
Doctors by advising treatment solutions, but also enables people to lead a
better and healthier lifestyle.